Student-Academic-Performance-Prediction

A Machine Learning project focused on predicting student academic performance using classification techniques. The project leverages student demographic, behavioural, and academic data to forecast outcomes such as grades or pass/fail status.

Key Features:

End-to-End ML Pipeline: Covers all stages of a typical machine learning workflow:

Data collection and cleaning

Exploratory Data Analysis (EDA)

Feature engineering and selection

Model training and hyperparameter tuning

Model evaluation and interpretation

Data-Driven Insights: Identifies which factors—such as study habits, attendance, parental background, and prior grades—most impact student performance.

Multiple Classification Models: Experiments with models like Logistic Regression, Random Forest, Gradient Boosting, and Support Vector Machines to find the most accurate predictor.

Robust Evaluation Metrics: Uses Accuracy, Precision, Recall, F1-score, and ROC-AUC to evaluate the model comprehensively.

Interpretability: Highlights important features to provide actionable insights for educators and administrators.

Implementation Steps:

Data Acquisition: Collect student datasets from schools, surveys, or publicly available sources.

Data Exploration: Analyse patterns, correlations, and trends in student attributes and outcomes.

Data Cleaning: Handle missing values, encode categorical variables, and normalize numerical data.

Feature Engineering: Create new features like average study hours, attendance ratio, or previous term performance.

Data Transformation: Convert categorical features into numeric representations using Label Encoding or One-Hot Encoding.

Train-Test Split: Split the dataset into training and testing sets to evaluate model performance.

Model Training: Train multiple classification models and tune hyperparameters for optimal accuracy.

Evaluation: Use multiple metrics to measure the performance and reliability of each model.

Prediction & Insights: Predict student outcomes and provide actionable insights for improving academic success.

Project Value:

Provides predictive insights into student performance, helping educators identify at-risk students early.

Demonstrates the application of machine learning for education analytics and data-driven decision-making.

Adds significant value to a Portfolio, showcasing skills in Data Preprocessing, Feature Engineering, Classification Modelling, and Model Interpretation.